Explore how Aidoc’s clinical AI solutions can increase hospital efficiency, show proven return on investment, and help enable better outcomes.
Learn moreDiscover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Learn moreSee the latest research, case studies, tips and more to start improving outcomes with healthcare AI today.
Learn moreLearn more about Aidoc’s approach, mission and leadership team that is revolutionizing healthcare with AI.
Learn moreExplore how Aidoc’s clinical AI solutions can increase hospital efficiency, show proven return on investment, and help enable better outcomes.
Learn moreDiscover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Learn moreSee the latest research, case studies, tips and more to start improving outcomes with healthcare AI today.
Learn moreLearn more about Aidoc’s approach, mission and leadership team that is revolutionizing healthcare with AI.
Learn more500 non-contrast enhanced CT’s of the brain performed in June, July and Aug. 2018 were independently analyzed on the presence of pathological hyperdensities by a deep learning software package and a fourth-year radiology resident. Their results were compared to a “gold standard analysis,” performed by a senior neuroradiologists.
Pathological hyperdensities were present in 134/500 patients, the majority of which were hemorrhages (128/134; 95.5%). Pathological hyperdensities were correctly identified by Aidoc software in 125/134 cases (93.3%), compared to 133/134 (99.3%) for the resident. Aidoc’s false-negative ratio was 9/134 (6.7%). When no pathological hyperdensities were present, the exam was rated negative by Aidoc software in 345/366 cases (94.3%), compared to 362/366 (98.9%) for the resident. Aidoc’s false-positive ratio was 21/366 (5.7%). The use of a deep learning algorithm for the detection of pathological intracranial hyper densities helped to detect urgent cases more quickly.
The AI prototype algorithm has a high degree of diagnostic accuracy for the detection of hyperdensities on CT. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness.
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